Model Evaluation Insights
Overfitting can create a false sense of security, as a model may achieve high accuracy on training data but fail in real-world scenarios. It's crucial to reserve a portion of data for testing to accurately assess the model's performance. A significant drop in accuracy from training to test data signals the need for reevaluation before deployment.In this clip
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